Radiogenomic Molecular Imaging Framework

• Radiogenomic molecular imaging analysis frameworks are integrative systems that fuse image-derived features with genomic, transcriptomic, and epigenetic data to reveal genotype–phenotype relationships.
• They employ diverse methods—such as bipartite graph learning, deep feature fusion, and neural–geometric models—to extract and integrate high-dimensional imaging and multi-omic signatures.
• These approaches enhance disease characterization, improve personalized diagnosis, and offer actionable mechanistic insights for translational precision medicine.

Radiogenomic molecular imaging analysis frameworks integrate high-dimensional medical imaging phenotypes with genomic, transcriptomic, and epigenetic data to enable discovery, interpretation, and prediction of disease-relevant molecular traits from non-invasive imaging. By fusing deep or classical image-derived features and multi-omic molecular signatures through graph-based, probabilistic, neural, or statistical models, radiogenomic analysis provides mechanistic insight into genotype–phenotype relationships, facilitates personalized diagnosis and therapeutic stratification, and supports interpretability essential for translational precision medicine.

1. Underlying Framework Types and Canonical Workflows

Radiogenomic molecular imaging analysis frameworks span multiple methodological axes defined by data modality, feature extraction, integration strategy, modeling architecture, and interpretability. Representative archetypes include:

• Heterogeneous Bipartite Graph Representation Learning (BGRL): Encodes gene–image relationships as bipartite graphs, dynamically computes edge weights between sMRI scan-derived embeddings and selected driver gene nodes (e.g., APOE, PSEN1, PSEN2), and propagates bipartite message-passing for multi-class disease staging (Raj et al., 14 May 2025).
• Multi-View Deep Feature Fusion: Parallel deep generative embedding streams (e.g., multi-view VAEs per imaging modality) yield modality-specific latent codes fused at a compact late-latent stage, minimizing redundancy and preserving unique contrast information (Miteva et al., 26 Dec 2025).
• Collaborative Neural–Geometric Learning: Combines 3D image learners (CNNs) and boundary geometric encoders (GNNs) with latent alignment regularization for joint molecular status prediction in solid tumors (Wei et al., 2022).

Each framework operates through a sequence:

1. Acquisition and harmonization of volumetric imaging and molecular datasets.
2. Preprocessing (registration, normalization, segmentation) and radiomic feature extraction: texture, shape, first-order statistics.
3. Modality-specific encoding (autoencoders, deep CNNs, graph-based embeddings, classical radiomics).
4. Feature- or graph-level fusion: concatenation, attention, or probabilistic modeling.
5. Supervised outcome modeling (classification, survival, regression) with clinical validation.
6. Interpretability via attribution (Grad-CAM, SHAP, permutation importance, edge scoring).

2. Feature Extraction and Fusion Approaches

Frameworks extract high-dimensional image features from sMRI, CT, PET, or multi-modality sources using combinations of deep and classical radiomic approaches:

• Radiomic Extraction: Histogram moments, GLCM/GLRLM/GLSZM/GLDM textures, fractal dimension, geometry (shape, centroid, axis lengths) (Jamil, 11 Jan 2026, Feng et al., 15 Oct 2025).
• Deep Representation Learning: 3D convolutional autoencoders (latent z_img ∈ ℝ^{512}), variational autoencoders (per-modality μ ∈ ℝ^6), ResNet/UNet feature maps, point cloud GNN embeddings (Raj et al., 14 May 2025, Miteva et al., 26 Dec 2025, Wei et al., 2022).
• Attention and Squeeze–Excitation: Channel, spatial, and variable-scale attention gates reweight informative features, enhancing representation fidelity (Jamil, 11 Jan 2026).

Fusion of imaging and molecular information follows:

• Early Fusion: Concatenation of image and molecular features with batch normalization prior to supervised classification (Jamil, 11 Jan 2026).
• Late Fusion: Classifier heads operate on deep image features and radiomic vectors in parallel, predictions ensembled or averaged (Kollias et al., 2023).
• Graph-Based Fusion: Image and gene nodes connected by dynamically weighted edges, message-passing updates jointly learned embeddings (Raj et al., 14 May 2025).
• Attention-Based Fusion: Cross-modal attention modules (queries/keys/values) selectively aggregate information from modalities into final representations (Oghenekaro, 30 Nov 2025).

3. Radiogenomic Integration and Predictive Modeling

Radiogenomic analysis typically leverages supervised and probabilistic models for disease trait prediction:

• Classification Heads: Multi-class SVMs, random forests, neural networks, or small MLPs map fused or graph embeddings to discrete staging (e.g., AD vs MCI vs CN, methylation status) (Raj et al., 14 May 2025, Miteva et al., 26 Dec 2025, Jamil, 11 Jan 2026).
• Survival Modeling: Cox proportional hazards regression on fused latent features or radiogenomic PCs for risk stratification (Oghenekaro, 30 Nov 2025, Islam et al., 2021, Mohammed et al., 2021).
• Graph Neural Networks: Explicit bipartite or multipartite models (no gene–gene/image–image edges), edge weight learning via per-sample adjacency matrices, node updating via gated message passing (Raj et al., 14 May 2025).
• Sparse Group Penalization: Multivariate sparse group lasso with coupling across imaging, genomics, and clinical models (inverse-weighting by cross-model coefficients), supports non-overlapping datasets and flexible outcome types (Zeng et al., 2022).
• Generative Modeling: Conditional GANs synthesize imaging from gene profiles (or vice versa), learning joint image–molecular embeddings and providing end-to-end radiogenomic maps (Xu et al., 2019).

4. Interpretability and Attribution Methods

Explainable Artificial Intelligence (XAI) methods are critical for clinical integration and mechanistic insight:

• Edge Importance Scoring: Averaging dynamic edge weights per gene across test samples reveals radiogenomic drivers ranked by their contribution to classification (Raj et al., 14 May 2025).
• Grad-CAM Attribution: Visualization of CNN activation gradients identifies imaging subregions (e.g., peripheral rim, peritumoral edema) most influential for molecular trait prediction (Jamil, 11 Jan 2026, Wei et al., 2022).
• SHAP and Feature Attribution: Kernel SHAP and similar techniques estimate Shapley values for all fused image and gene features, highlighting key texture or geometry metrics and facilitating hypothesis generation (Jamil, 11 Jan 2026, Feng et al., 15 Oct 2025).
• Statistical Group Selection: Bayesian spike-and-slab priors with estimated local FDR control select imaging predictors most strongly associated with molecular pathways (Mohammed et al., 2021).
• Saliency and Masking: Neural gradient-based gene saliency and in-silico masking are used to extract active transcriptomic modules that drive imaging traits, and those are correlated with outcome (Smedley et al., 2019).

5. Evaluation Metrics and Empirical Performance

Frameworks report robust multivariate performance across binary, multi-class, and survival tasks:

• Alzheimer’s Disease Radiogenomics (BGRL): AD vs CN classification: Acc. 92%, F1 93%, Rec. 100%, Prec. 87.5%; multi-class (AD/MCI/CN) macro-F1 ≈ 82.1% (Raj et al., 14 May 2025).
• Glioblastoma MGMT Methylation: Multi-view latent VAE fusion: RF AUC ≈ 0.77, outperforming unimodal and early-fusion baselines (Miteva et al., 26 Dec 2025). Hybrid radiomic–deep fusion: AUC = 0.871 ± 0.012, Acc. 0.866 (Jamil, 11 Jan 2026).
• Sparse Group Lasso (NSCLC): Survival AUC ≈ 0.65; joint model improves prediction error and TPR relative to classical lasso and SGL (Zeng et al., 2022).
• Spherical Radiomics: MGMT/EGFR/PTEN prediction: AUCs ≥ 0.80 (MGMT: 0.85); survival AUC: 0.83. Outperforms Cartesian radiomics (by ≥ 0.15 AUC) (Feng et al., 15 Oct 2025).
• Brain Tumor Radiogenomics (BTDNet): MGMT methylation macro F1 = 66.2% ± 3.1%, exceeding baseline deep multi-modal networks (Kollias et al., 2023).

Ablation studies consistently show that removal or degradation of integration (e.g., disabling dynamic edge-weight learning, omitting gene nodes) reduces accuracy by substantial margins (Raj et al., 14 May 2025).

6. Extensibility, Generalization, and Future Directions

Frameworks exhibit extensibility across modalities, molecular traits, and analytic configurations:

• Node/Feature Expansion: BGRL permits inclusion of PET nodes, CSF-biomarker nodes, SNP blocks, and multipartite graphs (Raj et al., 14 May 2025).
• Loss Function Adaptation: Regression (e.g., cognitive scores), survival, or multi-task objectives can be incorporated into radiogenomic analysis pipelines (Oghenekaro, 30 Nov 2025).
• Multi-modal Data Streams: Integration models (attention, cross-modal fusion) generalize from MRI to CT, PET, ultrasound, and histopathology images, as well as multi-omic vectors (genome-wide SNPs, single-cell RNA-seq) (Oghenekaro, 30 Nov 2025).
• Interpretability across Cancer Types: Gene/pathway selection, saliency, and attribution mechanisms scale from brain tumors to breast, lung, and others (Smedley et al., 2019, Oghenekaro, 30 Nov 2025).
• Radial and Layered Modeling: Spherical and concentric-layer radiomics capture evolutionary gradients of tumor heterogeneity and align imaging features to precise developmental/biological contexts (Mohammed et al., 2021, Feng et al., 15 Oct 2025).

Limitations include sample sizes, need for manual tumor annotation, and generalization beyond current imaging protocols. Future work will address full multi-omic integration, semi-supervised latent embedding regularization, and joint imaging-genomic segmentation–classification pipelines (Miteva et al., 26 Dec 2025, Jamil, 11 Jan 2026).

7. Clinical and Research Implications

Radiogenomic frameworks yield molecularly interpretable imaging biomarkers, which:

• Support non-invasive staging and diagnosis (AD/MSI/CN staging, MGMT/IDH/EGFR status).
• Enable personalized treatment stratification (e.g., MGMT methylation for temozolomide response).
• Enhance mechanistic understanding of disease evolution (layered imaging-to-genomics mapping, radial transitions).
• Allow robust, reproducible research through open-source deep learning tools (PyTorch/MONAI implementations, model checkpoints, standardized features) (Jamil, 11 Jan 2026, Raj et al., 14 May 2025).
• Facilitate clinical translation with real-time GUI overlays of molecular hot-spots, Grad-CAM, and feature-attribution bar-charts (Jamil, 11 Jan 2026).

The consensus across recent frameworks is that graph-based, generative, and attention-fused radiogenomic models are critical for unlocking genotype–phenotype relationships at scale, with interpretability enabled via edge scoring, saliency, and SHAP analysis (Raj et al., 14 May 2025, Jamil, 11 Jan 2026, Oghenekaro, 30 Nov 2025, Miteva et al., 26 Dec 2025).